Traditional methods of flight flutter testing analyze system parameters, such as damping levels, that vary with flight condition to monitor aircraft stability. (M. W. Kehoe, "A Historical Overview of Flight Flutter Testing," NASA-TM-4720, October 1995.) A real-time method to estimate the damping levels was developed based on a recursive prediction-error method. (R. Walker and N. Gupta, "Real-Time FLutter Analysis," NASA-CR-170412, March 1984.) This method was extended to improve the estimates by considering an extended Kalman filter in the formulation. (R. Roy and R. Walker, "Real-Time Flutter Identification," NASA-CR-3933, October 1985.) On-line methods using both time-domain and frequency domain characteristics of turbulence response data have also been formulated to estimate dampings. (C. L. Ruhlin et al., "Evaluation of Four Subcritical Response for On-Line Prediction of Flutter Onset in Wind Tunnel Tests," Journal of Aircraft, Vol. 20, No. 10, October 1983, pp. 835-840.) These methods monitored stability at test points, but they were of limited usefulness for predicting the onset of aeroelastic flutter because damping may be highly nonlinear as flight conditions vary, so damping trends may indicate stability despite proximity to an explosive flutter condition. An alternative eigenspace method was formulated based on orthogonality between eigenvectors, but this method uses a parameter that, similar to damping, indicates stability and may vary nonlinearly with flight condition. (D. Afolabi et al., "Flutter Prediction Using an Eigenvector Orientation Approach," AIAA Journal, Vol. 36, No. 1, January 1998, pp. 69-74.)
The concept of predicting the onset of flutter by analyzing flight data at subcritical airspeeds has been introduced in conjunction with a method for formulating a flutter margin envelope. (N. H. Zimmerman and J. T. Weissenburger, "Prediction of Flutter Onset Speed Based on Flight Testing at Subcritical Speeds," Journal of Aircraft, Vol. 1, No. 4, July-August 1964, pp. 190-202.) This method considered the interaction of two modes in the flutter mechanism to formulate a stability parameter that varied quadratically with dynamic pressure. This technique has been extended to consider several modes interacting as the flutter mechanism in order to demonstrate a prediction method for higher-order flutter. (S. J. Price and B. H. K. Lee, "Development and Analysis of Flight Flutter Prediction Methods," AIAA Dynamics Specialists Conference (Dallas, Tex.) AIAA-92-2101, April 1992, pp. 188-200; S. J. Price and B. H. K. Lee, "Evaluation and Extension of the Flutter-Margin Method for Flight Flutter Prediction," Journal of Aircraft, Vol. 30, No. 3, May-June 1993, pp. 395-402; and K. E. Kadrnka, "Multimode Instability Prediction Method," AIAA Structure, Structural Dynamics, and Materials Conference (Orlando, Fla.), AIAA-85-0737, April 19185, Volume 2, pp. 453-442.) These flutter margin testing techniques have been used for wind tunnel and flight test programs. (R. M. Bennett, "Applications of Zimmerman Flutter-Margin Criterion to a Wind-Tunnel Model," NASA-TM-84545, November 1982 and H. Katz et al., "F-15 Flight Flutter Test Program," Flutter Testing Techniques, NASA-SP-415, October 1975, pp. 413-431.) However, the method is of limited applicability for general flight flutter testing because the assumptions of few modes coupling and the requirement to observe those modes may be too restrictive.
Stability parameters were also introduced in determining flutter margins that consider an autoregressive moving average process to describe the aeroelastic dynamics. One parameter was based on determinants from a stability criterion for discrete-time systems that are excited by random turbulence. (Y. Matsuzaki and Y. Ando, "Estimation of Flutter Boundary from Random Responses Due to Turbulence at Subcritical Speeds," Journal of Aircraft, Vol. 18, No. 10, October 1981, pp. 862-868 and Y. Matsuzaki and Y. Ando, "Divergence Boundary Prediction from Random Responses; NAS's Method," Journal of Aircraft, Vol. 21, No. 6, June 1984, pp. 435-436.) A similar parameter was developed by extending the determinant method to consider short data segments with assumptions of local stationarity. (Y. Matsuzaki and Y. Ando, "Flutter and Divergence Boundary Prediction from Nonstationary Random Responses at Increasing Speeds," AIAA Structures, Structural Dynamics, and Materials Conference (Orlando, Fla.), AIAA-85-0691, April 1985, Vol. 2, pp. 313-320.) Another extension to this method derived a similar stability parameter but relaxed the requirements for stationariness. (H. Torii and Y. Matsuzaki, "Flutter Boundary Prediction Based on Nonstationary Data Measurement," Journal of Aircraft, Vol. 34, No. 3, May-June 1997, pp. 427-432.) These techniques of determining flutter margins can be applied to complex systems and require only turbulence for excitation; however, the flutter boundary is computed by extrapolating a nonlinear function and may be misleading.
In view of the foregoing, it is clear that in the past the actual flight envelope developed for aircraft operation is essentially determined only by flight testing. The edges of the envelope are points where either the aircraft cannot fly any faster because of engine limitations or, with a 15% margin for error, where the damping trends indicate a flutter instability may be near. After flight testing, the envelope thus empirically determined is used for regular operations. It would be desirable to use both the aircraft model computations and the test flight data in determining flutter margins in order to provide a more expanded and robust flutter margin envelope.